Prediction of grain size distribution using ordinary kriging and compositional kriging methods

Abstract

Grain size analysis plays a crucial role in understanding the geological characteristics of the coastal environments that influence the optimizations for oil and gas production operations. This paper aims to explore a sophisticated geostatistical approach using the ordinary kriging and compositional kriging techniques, to forecast the grain size fluctuations of sediments in the Long Island region located in the United States. In addition, utilizing a comprehensive dataset collected from the same region about an integrated seventeen compositional components for investigation using the spatial model of the grain size distribution. Moreover, a variogram and the scatter plot predicted a distinctive spatial dependency was achieved. The compositional kriging method used to predict the grain size distribution in the coastal areas presented an accurate result based on the shape of the histogram, Root Mean Square Error (RMSE), and the Mean Squared Error (MSE). In conclusion, the geo-statistics assisted in the integration of the sedimentological analysis in the coastal settings and showed an effective configuration for the decision-making in the oil and gas industry business.